基于网络药理学和分子对接方法对逍遥散、越鞠丸、柴胡疏肝散、半夏白术天麻汤“同病异治”抑郁症作用机制的探索研究
Study on the Mechanism of Treating Depression with “the Same Disease with Different Treatments” by Xiaoyao Powder, Yueju Pill, Chaihu Shugan Powder and Banxia Baizhu Tianma Decoction Based on Network Pharmacology and Molecular Docking Methods

抑郁症严重损害个人、家庭及社会健康，抗抑郁的相关研究需要更加全面与深入。目的：利用网络药理学揭示逍遥散、越鞠丸、柴胡疏肝散、半夏白术天麻汤“同病异治”抑郁症的作用机制，并用分子对接方法进一步验证中药抗抑郁的有效化学成分。方法：通过TCMSP、BATMAN-TCM以及ETCM数据库筛选逍遥散、越鞠丸、柴胡疏肝散、半夏白术天麻汤的药物活性化学成分及靶点；检索GeneCards、OMIM、DisGeNET疾病数据库及对基因芯片GSE151807部分数据进行基因差异分析筛选抑郁症靶点；使用String和Cytoscape3.8.2工具构建药物–疾病共有靶点蛋白质互作PPI网络以及共有活性成分–药物靶点–疾病靶点网络，通过GO、KEGG、拓扑分析及度值分析探索抗抑郁关键靶点与中药方剂中重要的抗抑郁活性化学成分，以期揭示四种中药方剂“同病异治”抑郁症的科学内涵。结果：ACTB、INS、AKT1、ALB、IL6、TNF、BDNF、TP53、MAPK3、IL1B为四种中药方剂“同病异治”抑郁症度值前十的关键共有靶点。STAT3、EIF4E、EP300、WT1、IGF1R、HNF1B、PSEN1、DDX3X、DICER1、PAX8为四种中药方剂“同病异治”抑郁症度值前十的关键特有靶点。共有活性成分–药物靶点–疾病靶点网络分析结果表示Nootkatone (圆柚酮)、Carvone (香芹酮)，Hexadecanoic Acid (十六烷酸)、Alpha-Pinene (α-蒎烯)，Quercetin (槲皮苷)、Stigmasterol (豆甾醇)分别为两两共有、三三共有、四四共有活性成分中度值排序前二的有效化学成分。通过分子对接的验证，最终发现ACTB-槲皮苷、ACTB-豆甾醇、TNF-槲皮素、TNF-豆甾醇四种对接模型的结合最为稳定，提示槲皮苷与豆甾醇可以作用于ACTB与TNF来达到抗抑郁效果。结论：四种中药的药物靶点可以通过作用于相同的疾病靶点而参与抗抑郁相关通路，进而达到防治抑郁症的作用，同时，这些药物特有的靶点也可以通过作用于疾病靶点或更多潜在未发现的疾病靶点抗抑郁。综上，槲皮苷与豆甾醇可以作用于ACTB与TNF来达到抗抑郁效果。
Depression seriously damages the health of individuals, families and society, the related research on anti-depression needs to be more comprehensive and in-depth. Objective: Use network pharmacology to reveal the mechanism of treating depression with “the same disease with different treatments” by Xiaoyao Powder, Yueju Pill, Chaihu Shugan Powder and Banxia Baizhu Tianma Decoction, and to further verify the effective anti-depression chemical components of traditional Chinese medicine by molecular docking method. Methods: The active chemical components and targets of Xiaoyao powder, Yueju pill, Chaihu Shugan powder and Banxia Baizhu Tianma decoction were screened by TCMSP, BATMAN-TCM and ETCM databases. Search GeneCards, OMIM, DisGeNET disease databases and screen depression targets by gene difference analysis of gene chip GSE151807; by using the tools of String and Cytoscape3.8.2, the PPI network of drug-disease common targets and the network of common active ingredients-drug targets-disease targets were constructed. Through GO, KEGG, topology analysis and degree analysis, the key anti-depression targets and important active chemical components in traditional Chinese medicine prescriptions were explored to reveal the scientific connotation of “the same disease with different treatments” in four traditional Chinese medicine prescriptions. Results: ACTB, INS, AKT1, ALB, IL6, TNF, BDNF, TP53, MAPK3, IL1B were the key common targets in the top ten depression values of the four traditional Chinese medicine prescriptions. STAT3, EIF4E, EP300, WT1, IGF1R, HNF1B, PSEN1, DDX3X, DICER1 and PAX8 were the key specific targets in the top ten